Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPulse Shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II
126
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Pulse shape discrimination plays a key role in improving the signal-to-background ratio in NEOS analysis by removing fast neutrons. Identifying particles by looking at the tail of the waveform has been an effective and plausible approach for pulse shape discrimination, but has the limitation in sorting low energy particles. As a good alternative, the convolutional neural network can scan the entire waveform as they are to recognize the characteristics of the pulse and perform shape classification of NEOS data. This network provides a powerful identification tool for all energy ranges and helps to search unprecedented phenomena of low-energy, a few MeV or less, neutrinos.
rate research
Read More
Using the waveforms from a digital electronic system, an offline analysis technique on pulse shape discrimination (PSD) has been developed to improve the neutron-gamma separation in a bar-shaped NE-213 scintillator that couples to a photomultiplier tube (PMT) at each end. The new improved method, called the ``valued-assigned PSD (VPSD), assigns a normalized fitting residual to every waveform as the PSD value. This procedure then facilitates the incorporation of longitudinal position dependence of the scintillator, which further enhances the PSD capability of the detector system. In this paper, we use radiation emitted from an AmBe neutron source to demonstrate that the resulting neutron-gamma identification has been much improved when compared to the traditional technique that uses the geometric mean of light outputs from both PMTs. The new method has also been modified and applied to a recent experiment at the National Superconducting Cyclotron Laboratory (NSCL) that uses an analog electronic system.
The fast neutron rate is measured at the site of NEOS experiment, a short baseline neutrino experiment located in a tendon gallery of a commercial nuclear power plant, using a 0.78-liter liquid scintillator detector. A pulse shape discrimination technique is used to identify neutron signals. The measurements are performed during the nuclear reactor-on and off periods and found to be ~20 per day for both periods. The fast neutron rate is also measured at an overground site with a negligible overburden and is found to be ~100 times higher than that at the NEOS experiment site.
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5%. This uncertainty is due to differences between signal and calibration samples.
We report on the characterization of two inverted coaxial Ge detectors in the context of being employed in future $^{76}$Ge neutrinoless double beta ($0 ubetabeta$) decay experiments. It is an advantage that such detectors can be produced with bigger Ge mass as compared to the planar Broad Energy Ge detectors (BEGe) that are currently used in the GERDA $0 ubetabeta$ decay experiment. This will result in lower background for the search of $0 ubetabeta$ decay due to a reduction of cables, electronics and holders. The measured resolution near the $^{76}$Ge Q-value at 2039 keV is 2.5 keV and their pulse-shape characteristics are similar to BEGe-detectors. It is concluded that this type of Ge-detector is suitable for usage in $^{76}$Ge $0 ubetabeta$ decay experiments.
The GERDA experiment located at the LNGS searches for neutrinoless double beta (0 ubetabeta) decay of ^{76}Ge using germanium diodes as source and detector. In Phase I of the experiment eight semi-coaxial and five BEGe type detectors have been deployed. The latter type is used in this field of research for the first time. All detectors are made from material with enriched ^{76}Ge fraction. The experimental sensitivity can be improved by analyzing the pulse shape of the detector signals with the aim to reject background events. This paper documents the algorithms developed before the data of Phase I were unblinded. The double escape peak (DEP) and Compton edge events of 2.615 MeV gamma rays from ^{208}Tl decays as well as 2 ubetabeta decays of ^{76}Ge are used as proxies for 0 ubetabeta decay. For BEGe detectors the chosen selection is based on a single pulse shape parameter. It accepts 0.92$pm$0.02 of signal-like events while about 80% of the background events at Q_{betabeta}=2039 keV are rejected. For semi-coaxial detectors three analyses are developed. The one based on an artificial neural network is used for the search of 0 ubetabeta decay. It retains 90% of DEP events and rejects about half of the events around Q_{betabeta}. The 2 ubetabeta events have an efficiency of 0.85pm0.02 and the one for 0 ubetabeta decays is estimated to be 0.90^{+0.05}_{-0.09}. A second analysis uses a likelihood approach trained on Compton edge events. The third approach uses two pulse shape parameters. The latter two methods confirm the classification of the neural network since about 90% of the data events rejected by the neural network are also removed by both of them. In general, the selection efficiency extracted from DEP events agrees well with those determined from Compton edge events or from 2 ubetabeta decays.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
